Kunihito Hoki, a Japanese chemist, computer scientist, and since 2010 Assistant Professor at the University of Electro-Communications. Kunihito Hoti received a D.Sc. in Chemistry from Tohoku University, and has been a research fellow at the University of Toronto as well as Tohoku University, where he then became Assistant Professor [1]. As Shogi programmer, he is author of the strong Shogi program Bonanza, famous for its supervised move adaption tuning method called Minimax Tree Optimization (MMTO), also dubbed the Bonanza-Method.
Kunihito Hoki [2]

Selected Publications

• Kunihito Hoki (2006). Optimal control of minimax search result to learn positional evaluation. 11th Game Programming Workshop (Japanese)
• Takuya Obata, Takuya Sugiyama, Kunihito Hoki, Takeshi Ito (2010). Consultation Algorithm for Computer Shogi: Move Decisions by Majority. CG 2010
• Takuya Sugiyama, Takuya Obata, Kunihito Hoki, Takeshi Ito (2010). Optimistic Selection Rule Better Than Majority Voting System. CG 2010
• Tomoyuki Kaneko, Kunihito Hoki (2011). Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes. Advances in Computer Games 13
• Kunihito Hoki, Tomoyuki Kaneko (2011). The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search. Advances in Computer Games 13
• Kunihito Hoki, Tomoyuki Kaneko, Akihiro Kishimoto, Takeshi Ito (2013). Parallel Dovetailing and its Application to Depth-First Proof-Number Search. ICGA Journal, Vol. 36, No. 1 ^[4]
• Kunihito Hoki, Tomoyuki Kaneko (2014). Large-Scale Optimization for Evaluation Functions with Minimax Search. JAIR Vol. 49, pdf » Automated Tuning, Shogi ^[5]

External Links

• Kunihito Hoki's ICGA Tournaments
• 保木 邦仁 / Kunihito Hoki – TEDxKids@Chiyoda 2012

References

1. ^ Recent advances in computer shogi and the Bonanza program | Department of Computing Science, University of Alberta, February 3, 2012
2. ^ 保木 邦仁 / Kunihito Hoki – TEDxKids@Chiyoda 2012
3. ^ dblp: Kunihito Hoki
4. ^ Dovetailing (computer science) from Wikipedia
5. ^ MMTO for evaluation learning by Jon Dart, CCC, January 25, 2015

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